We have been working with digital twins since their early incarnations in the late 2000s and have learnt a lot. In this Eigen Perspective we cut through the hype, bust some myths and present a more measured case for digital twin technology.
Digital twin technology relies on cloud computing which means the scope for simulating the real-world increases dramatically.
As more data, Internet of Things-enabled equipment and applications can be integrated and automated – to perform faster and more complex calculations, with increasingly sophisticated visualisation.
Digital twin is the realisation of the digital oilfield technology vision of the early 2000s, when Eigen first began working with bp on its first implementation in Azerbaijan.
It’s no exaggeration to say that in complex, physics-based industries like oil and gas, digital twin technology is probably the biggest technology game-changer since seismic imaging.
Few think it’s a short term craze: growth in the digital twin market is forecast across all sectors globally from $3bn in 2020 to $48bn in 2026, driven largely by the need to reduce costs (design, build and maintenance) and recover from, and respond to, the pandemic (Markets and Markets, 2020)
Myth 1: Digital twin is just visualisation and simulation; the rest is Emperor’s New Clothes.
Visualisation and simulation are key components of any digital twin, enabled and powered by giant leaps in cloud computing and capacity to operate near-real-time. But the game-changer in making digital twins a reality is knowledge graph technology.
At Eigen we use knowledge graph technology specialist Neo4j to build domain-specific data models, e.g. safety barrier data models, that act as the brain of the digital twin, calling on specific data or calculations to run specific actions e.g. simulations, visualisation, alerts or events.
Powered by the cloud and knowledge graph technology, the digital twin has emerged as one of the biggest technology breakthroughs in a generation, transforming industries as varied as pharmaceuticals, automotive and oil and gas. It’s far from Emperor’s New Clothes.
Myth 2: The digital twin is the Oracle, all-knowing, all powerful.
Digital twin technology is undoubtedly powerful, but like most technologies, it is limited by the quality and accuracy of the data it can access, how its various components have been designed – and how humans interact with them.
We wouldn’t characterise the satnav as all-knowing because it can access a data model (or knowledge graph) to optimise our route from A to B. The same is true for digital twin technology: the user sets the objective, defines any parameters and conditions, and ultimately chooses whether to accept or ignore the advice.
A good example of this is where Eigen worked with Lundin to build a digital twin of its blowdown system, using the Ingenuity platform. Blowdown valves on plant are designed to depressure equipment fast, to make it safe.
Blowdown performance must meet industry standards and is subject to safety audit.
Without the digital twin, managing blowdowns was a time-consuming manual job, requiring data gathering from multiple documents and systems, exporting to Excel for analysis and pasting data and charts into Word-based reports.
With the blowdown digital twin the Lundin operations team are now only alerted when a blowdown occurs that does not meet agreed performance criteria. The team can access a live, online report of all previous blowdown performance providing full history for analysis and audit.
While the digital twin is not all-knowing, it provides assurance, and frees up high cost engineering time for more value adding work.
Myth 3: You only capture value from a digital twin that covers your entire asset.
In many ways, this kind of monolithic thinking has halted progress of digital transformation: the comforting, but ultimately false, logic that housing all data and systems in one place is the only route to control.
Systems, like those for oil and gas operations, are simply too big, too complex and too dynamic to be designed and operated as a single digital twin.
Moreover, value would haemorrhage from any such project, owing to time lost in design, as well as overlooked low hanging fruit.
At Eigen we work with clients to solve a specific problem or use-case, using technology to bring information and insight to the client to accelerate decision-making and drive team productivity. Then we work incrementally, use-case by use-case to extend the capabilities of the digital twin.
For instance, we built a maintenance backlog digital twin for one North Sea operator, enabling them for the first time to calculate and visualise barrier risk and use this to help prioritise and plan maintenance. Using the digital twin, the operator reduced the maintenance backlog on Safety & Environmentally Critical Equipment by 90% in three weeks
Myth 4: There’s a digital twin one-stop-shop who can meet all your needs.
Unfortunately there’s not, despite what some vendors may imply. At least not for the physics-based world of oil and gas. Scale and complexity prohibit such a thing, but perhaps of more significance is the specialist know-how layered into every piece of equipment within oil and gas systems, which no generalist vendor could replicate.
For example, the sophistication of a digital twin built by the original equipment manufacturer of a compressor would far exceed any digital twin a generalist could design. And why would it, when all it needs to know is a limited subset of the data the compressor produces when it shuts down, needs attention or starts up?
Digital twins therefore are the aggregation and integration of multiple digital twins, with the least redundancy and duplication to perform fast and accurately. Eigen for instance works with multiple specialist vendors to take specific data they produce to feed its digital twins. With bp, Eigen’s Ingenuity platform takes work order data from Maximo, to feed its Safety Barrier Health digital twin. Similarly, we integrate with OSISoft’s PI historian to point to operators’ temperature, pressure and flow rate data. And we access data from plant and equipment direct from ABB and Honeywell control systems.
There’s simply no value for the client in copying data into the digital twin, when it can be linked and its integrity managed by an expert. Rather than a digital twin vendor, clients tell us they see Eigen as the ‘glue’ that integrates their digital twins to drive efficiency, productivity and improved levels of safety
Myth 5: Digital twins only tell you about the present and the future.
Digital twin technologies can certainly help inform users what’s going on right now – and through simulation and visualisation can help predict the future. But they increasingly enable us to look back and retain “project memory”.
We are now just beginning to see the first oil and gas projects come online that have been fully designed as a digital twin, through concept development, design and build – and are now moving into operation equipped with digital twin technologies.
This means for the first time, we are also able to use digital twin technology to look back to prior decisions, previous designs, engineering rationales etc; the digital twin becomes the repository of project memory.
We have been working with one major operator on its latest project in the Gulf of Mexico, to build a digital twin of its safety barriers – this will enable us to look backwards, as well as in real and future time, harnessing the power of project memory as well as predictive capability. We have also built something similar for the new Edvard Grieg tiebacks Solveig and Rolvsnes, operated by Lundin Energy.
Myth 6: Digital twins rely on advanced AI.
There is undoubtedly a role for artificial intelligence in equipping digital twins with even more advanced advisory capabilities, particularly making recommendation to users.
By interrogating huge volumes of safety, operations, subsurface, production and project data AI could doubtless improve early warnings or identify new opportunities that are challenging for a human.
Yet, today’s digital twins, equipped with today’s technologies (Neo4j, Elastic, Docker, Python scripts and Eigen’s Ingenuity platform), are able to integrate massive volumes of data from multiple sources, and systems and applications in real-time, with sophisticated visualisation – and interfaces designed around the user (e.g. Search, Mobile, Screen Presenter, Interactive Dashboard).
These integrate to deliver low hanging fruit, like converting a document manually prepared each week of copy/ pasted data and charts into an online, mostly automated report for Lundin.
And also giant leaps in digital transformation, like building one of the most sophisticated safety barriers digital twins in the industry, used by Vår, Lundin and bp.
Myth 7: Digital twin technology is only an option for the big players.
Digital twin technology is not cheap nor is it a one-off investment. But because it can often deliver a quick return and payback, it is an accessible technology option for all operators, regardless of size.
In many ways digital twin technology is the digital transformation, and as such has to be evaluated as an investment to deliver future value from cost savings, efficiencies, accelerated production – and potentially new resources.
Many operators we work with at Eigen begin fearing a wholesale change of systems and processes, and while this can happen over time – and indeed may be necessary – we favour more incremental implementation, solving one use-case at a time, demonstrating value along the transformation journey.
As the Markets and Markets digital twin report (2020) identified, cost reduction is a major incentive for investment in digital twin technology. When the true cost of many manual jobs is calculated, investment in digital twin technology, particularly problem-solving use-case by use-case, often has a compelling return and quick payback.